Search

Modern text-to-speech voices can convey social cues ideal for narrating multimedia learning materials. Amazon Alexa has a unique feature among modern text-to-speech vocalizers as she can infuse enthusiasm cues into her synthetic voice. In this first study examining modern text-to-speech voice enthusiasm effects in a multimedia learning environment, a between-subjects online experiment was conducted where learners from a large Asian university (n = 244) listened to either Alexa's: (1) neutral voice, (2) low-enthusiastic voice, (3) medium-enthusiastic voice, or (4) high-enthusiastic voice, narrating a multimedia lesson on distributed denial-of-service attack. While Alexa's enthusiastic voices did not enhance persona ratings compared to Alexa's neutral voice, learners could infer more enthusiasm expressed by Alexa's medium-and high-enthusiastic voices than Alexa's neutral voice. Regarding cognitive load, Alexa's low-and high-enthusiastic voices decreased intrinsic and extraneous cognitive load ratings compared to Alexa's neutral voice. While Alexa's enthusiastic voices did not impact affective-motivational ratings differently from Alexa's neutral voice, learners reported a significant increase of positive emotions from their baseline positive emotions after listening to Alexa's medium-enthusiastic voice. Finally, Alexa's enthusiastic voices did not enhance the learning performance on immediate retention and transfer tests compared to Alexa's neutral voice. This study demonstrates that a modern text-to-speech voice enthusiasm can positively affect learners' emotions and cognitive load during multimedia learning. Theoretical and practical implications are discussed through the lens of the Cognitive Affective Model of E-learning, Integrated-Cognitive Affective Model of Learning with Multimedia, and Cognitive Load Theory. We further outline this study's limitations and recommendations for extending and widening the text-to-speech voice emotions research.

This study attempts to assess individuals' Bitcoin, Ethereum and Litecoin literacy, and then identifies different profiles based on the literacy levels of these cryptocurrencies, and further investigates whether financial literacy explains Bitcoin, Ethereum and Litecoin literacy. Using a sample from higher learning institutions in Malaysia, the results indicate that individuals exhibit higher knowledge on Bitcoin compared to Ethereum and Litecoin. Cluster analysis revealed two clusters, where the high-cryptocurrency-literacy cluster (high in Bitcoin, Ethereum, and Litecoin literacy) has a distinct demographic profile and higher financial literacy than the low-cryptocurrency-literacy cluster (low in Bitcoin, Ethereum, and Litecoin literacy). Financial literacy is shown to explain Bitcoin and Ethereum literacy, but not Litecoin literacy.